EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications

Random Forests

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Deep learning

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MizAR 60 for Mizar 50

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XGBoost: A Scalable Tree Boosting System

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Untitled work

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The Elements of Statistical Learning

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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

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A Unified Approach to Interpreting Model Predictions

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Artificial intelligence: a modern approach

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Visualizing and understanding convolutional networks

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Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works

Learning Deep Features for Discriminative Localization

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428 · see citing works

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works

From local explanations to global understanding with explainable AI for trees

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551 · see citing works

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works

Explaining and Harnessing Adversarial Examples

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Intriguing properties of neural networks

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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works

17. A Value for n-Person Games

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“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294 · see citing works

A Survey on Bias and Fairness in Machine Learning

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · see citing works

The mythos of model interpretability

Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works

Explanation in artificial intelligence: Insights from the social sciences

Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works

EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces

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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

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Some Studies in Machine Learning Using the Game of Checkers

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A study of the behavior of several methods for balancing machine learning training data

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The symbol grounding problem

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Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks

Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00097 · Cited by 3,639 · see citing works

Neural Networks and the Bias/Variance Dilemma

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Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.03.012 · Cited by 3,291 · see citing works

How the machine “thinks”: Understanding opacity in machine learning algorithms

Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 1× · see citing works

Towards A Rigorous Science of Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works

Explainable AI: a review of machine learning interpretability methods

Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020) · Entropy · doi:10.3390/e23010018 · Cited by 2,922 · see citing works

An Introduction to Statistical Learning: with Applications in R

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Methods for interpreting and understanding deep neural networks

Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665 · see citing works

Automated detection of COVID-19 cases using deep neural networks with X-ray images

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A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524 · see citing works

Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

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Explaining Explanations: An Overview of Interpretability of Machine Learning

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One Pixel Attack for Fooling Deep Neural Networks

Su, J., Vargas, D. V., & Sakurai, K. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2890858 · Cited by 2,406 · see citing works

Understanding deep learning (still) requires rethinking generalization

Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2021) · Communications of the ACM · doi:10.1145/3446776 · Cited by 2,400 · see citing works

Explaining prediction models and individual predictions with feature contributions

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Definitions, methods, and applications in interpretable machine learning

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works

Anchors: High-Precision Model-Agnostic Explanations

Ribeiro, M. T., Singh, S., & Guestrin, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11491 · Cited by 2,162 · cited 1× · see citing works

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery

Lundberg, S. M., Nair, B., Vavilala, M. S., Horibe, M., Eisses, M. J., Adams, T., Liston, D. E., Low, D. K.-W., Newman, S.-F., Kim, J., et al. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0304-0 · Cited by 1,967 · see citing works

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

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The false hope of current approaches to explainable artificial intelligence in health care

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CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning

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Computational Complexity: A Modern Approach

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Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission

Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685 · see citing works

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

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Effective gene expression prediction from sequence by integrating long-range interactions

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Predictive learning via rule ensembles

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Deep learning: new computational modelling techniques for genomics

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Exploring the Limits of Language ModelingPreprint

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Deep Neural Networks and Tabular Data: A Survey

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Explaining individual predictions when features are dependent: More accurate approximations to Shapley values

Aas, K., Jullum, M., & Løland, A. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103502 · Cited by 1,159 · see citing works

Explainable artificial intelligence: A survey

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Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080 · see citing works

A Unifying Review of Deep and Shallow Anomaly Detection

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Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis

Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. (2019) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.105405 · Cited by 1,065 · see citing works

Drug discovery with explainable artificial intelligence

Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063 · see citing works

Unmasking Clever Hans predictors and assessing what machines really learn

Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019) · Nature Communications · doi:10.1038/s41467-019-08987-4 · Cited by 1,047 · see citing works

A Survey on the Explainability of Supervised Machine Learning

Burkart, N., & Huber, M. F. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12228 · Cited by 1,008 · see citing works

Visual interpretability for deep learning: a survey

Zhang, Q., & Zhu, S. C. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700808 · Cited by 975 · see citing works

Attention is not not Explanation

Wiegreffe, S., & Pinter, Y. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1002 · Cited by 966 · see citing works

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Liao, Q. V., Gruen, D., & Miller, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376590 · Cited by 925 · see citing works

Transformer Interpretability Beyond Attention Visualization

Chefer, H., Gur, S., & Wolf, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00084 · Cited by 903 · see citing works

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3375627.3375830 · Cited by 900 · see citing works

LayerCAM: Exploring Hierarchical Class Activation Maps for Localization

Jiang, P.-T., Zhang, C., Hou, Q., Cheng, M., & Wei, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3089943 · Cited by 899 · see citing works

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Oh, Y., Park, S. J., & Ye, J. C. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2993291 · Cited by 897 · see citing works

From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

Ahmed, I., Jeon, G., & Piccialli, F. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3146552 · Cited by 853 · see citing works

A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

Mohseni, S., Zarei, N., & Ragan, E. D. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3387166 · Cited by 785 · see citing works

Noise : a flaw in human judgment

Kahneman, D. J., Sibony, O., & Sunstein, C. R. (2021) · Cited by 749 · see citing works

Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011-2022)

Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107161 · Cited by 734 · see citing works

The structure and function of explanations.

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Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis

Chen, R. J., Lu, M. Y., Wang, J., Williamson, D. F. K., Rodig, S. J., Lindeman, N. I., & Mahmood, F. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3021387 · Cited by 729 · see citing works

Manipulating and Measuring Model Interpretability

Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445315 · Cited by 728 · see citing works

Computational intelligence: a logical approach

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iml: An R package for Interpretable Machine Learning

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Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond

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Not Just a Black Box: Learning Important Features Through Propagating Activation DifferencesPreprint

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Model-Agnostic Interpretability of Machine Learning

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Controlling the false discovery rate via knockoffs

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Analysis of regression in game theory approach

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Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

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Captum: A unified and generic model interpretability library for PyTorch

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Explainability in Graph Neural Networks: A Taxonomic Survey

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Explainable machine learning in deployment

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A Survey of Methods for Explaining Black Box Models

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Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays

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Evaluating the quality of machine learning explanations: a survey on methods and metrics

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A Multiscale Visualization of Attention in the Transformer Model

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Notions of explainability and evaluation approaches for explainable artificial intelligence

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HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection

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Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers

Hohman, F., Kahng, M., Pienta, R., & Chau, D. H. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2843369 · Cited by 582 · see citing works

An Efficient Explanation of Individual Classifications using Game Theory

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Accurate intelligible models with pairwise interactions

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Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., & Wortman Vaughan, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376219 · Cited by 564 · see citing works

ERASER: A Benchmark to Evaluate Rationalized NLP Models

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